Abstract
This study presents a comprehensive deep learning pipeline for the automated classification of 12 foraminifera species using 2D micro-CT slices derived from 3D scans. We curated a scientifically rigorous dataset comprising 97 micro-CT scanned specimens across 27 species, selecting 12 species with sufficient representation for robust machine learning. To ensure methodological integrity and prevent data leakage, we employed specimen-level data splitting, resulting in 109,617 high-quality 2D slices (44,103 for training, 14,046 for validation, and 51,468 for testing). We evaluated seven state-of-the-art 2D convolutional neural network (CNN) architectures using transfer learning. Our final ensemble model, combining ConvNeXt-Large and EfficientNetV2-Small, achieved a test accuracy of 95.64%, with a top-3 accuracy of 99.6% and an area under the ROC curve (AUC) of 0.998 across all species. To facilitate practical deployment, we developed an interactive advanced dashboard that supports real-time slice classification and 3D slice matching using advanced similarity metrics, including SSIM, NCC, and the Dice coefficient. This work establishes new benchmarks for AI-assisted micropaleontological identification and provides a fully reproducible framework for foraminifera classification research, bridging the gap between deep learning and applied geosciences.
Abstract (translated)
这项研究提出了一种全面的深度学习管道，用于使用从3D扫描中提取的2D微CT切片自动分类12个有孔虫物种。我们整理了一个科学严谨的数据集，包含来自27个物种的97个微CT扫描样本，并选择了具有足够代表性的12个物种来进行稳健的机器学习研究。为了确保方法论上的完整性并防止数据泄露，我们在标本级别进行了数据分割，最终产生了109,617张高质量的2D切片（训练集44,103张、验证集14,046张和测试集51,468张）。 我们使用迁移学习评估了七种最先进的二维卷积神经网络(CNN)架构。我们的最终集成模型结合了ConvNeXt-Large和EfficientNetV2-Small，在所有物种上实现了95.64%的测试准确率，top-3准确率为99.6%，以及0.998的ROC曲线下的面积(AUC)。 为了便于实际部署，我们开发了一个交互式高级仪表板，支持实时切片分类和使用SSIM（结构相似性指数）、NCC（归一化互相关系数）及Dice系数等高级相似度指标进行3D切片匹配。这项工作为AI辅助的微古生物学鉴定设定了新的基准，并提供了一个完全可重复的研究框架，将深度学习与应用地球科学相结合。
URL
https://arxiv.org/abs/2512.00912
